Category Archives: Machine Learning

The article “Model-based Methods for Continuous and Discrete Global Optimization” by T. Bartz-Beielstein and M. Zaefferer is available online: http://www.sciencedirect.com/science/article/pii/S1568494617300546
A preprint can be downloaded from “Cologne Open Science”: urn:nbn:de:hbz:832-cos4-4356Abstract:The use of surrogate models is a standard method for dealing with complex real-world optimization problems. The first surrogate models were applied to continuous optimization problems. In recent years, surrogate models gained importance for discrete optimization problems. This article takes this development into consideration. The first part presents a survey of model-based methods, focusing on continuous optimization. It introduces a taxonomy, which is useful as a guideline for selecting adequate model-based optimization tools. The second part examines discrete optimization problems. Here, six strategies for dealing with discrete data structures are introduced. A new approach for combining surrogate information via stacking is proposed in the third part. The implementation of this approach will be available in the open source R package SPOT2. The article concludes with a discussion of recent developments and challenges in continuous and discrete application domains.

Industry is faced with solving complex optimization problems on a day to day basis in different domains including transportation, data mining, computer vision, computer security, robotics and scheduling amongst others. #machinelearning and search algorithms play an important role in solving such problems.
More: http://titancs.ukzn.ac.za/CIMADA2017.aspx

The authors (Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer and Michael K. Reiter) of the paper “Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition” [1] describe a new class of attacks that target face recognition systems: attacks that are 1. physically realizable and 2. at the same time are inconspicuous.
The authors investigate two categories of attacks: 1. dodging attacks (the attacker seeks to have her face misidentified as any other arbitrary face) and 2. impersonation attacks (the adversary seeks to have a face recognized as a specific other face). Their approach is based on the observation that Deep Neural Networks can be misled by mildly perturbing inputs [2]. More: https://www.cs.cmu.edu/~sbhagava/